Attention-Based Model and Deep Reinforcement Learning for Distribution of Event Processing Tasks

TL;DR

This paper introduces an attention-based deep reinforcement learning model for fair and scalable distribution of event processing tasks in IoT edge devices, outperforming traditional heuristics in key metrics.

Contribution

It presents a novel Transformer and Pointer Network-based neural network trained with reinforcement learning for scalable load balancing in IoT environments.

Findings

Outperforms conventional heuristics in key performance metrics

Scales effectively without hyperparameter re-tuning or retraining

Potential applicability to various load balancing scenarios

Abstract

Event processing is the cornerstone of the dynamic and responsive Internet of Things (IoT). Recent approaches in this area are based on representational state transfer (REST) principles, which allow event processing tasks to be placed at any device that follows the same principles. However, the tasks should be properly distributed among edge devices to ensure fair resources utilization and guarantee seamless execution. This article investigates the use of deep learning to fairly distribute the tasks. An attention-based neural network model is proposed to generate efficient load balancing solutions under different scenarios. The proposed model is based on the Transformer and Pointer Network architectures, and is trained by an advantage actor-critic reinforcement learning algorithm. The model is designed to scale to the number of event processing tasks and the number of edge devices, with no need for hyperparameters re-tuning or even retraining. Extensive experimental results show that the proposed model outperforms conventional heuristics in many key performance indicators. The generic design and the obtained results show that the proposed model can potentially be applied to several other load balancing problem variations, which makes the proposal an attractive option to be used in real-world scenarios due to its scalability and efficiency.